
<h1><span class="yiyi-st" id="yiyi-12">numpy.fft.ihfft</span></h1>
        <blockquote>
        <p>原文：<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.fft.ihfft.html">https://docs.scipy.org/doc/numpy/reference/generated/numpy.fft.ihfft.html</a></p>
        <p>译者：<a href="https://github.com/wizardforcel">飞龙</a> <a href="http://usyiyi.cn/">UsyiyiCN</a></p>
        <p>校对：（虚位以待）</p>
        </blockquote>
    
<dl class="function">
<dt id="numpy.fft.ihfft"><span class="yiyi-st" id="yiyi-13"> <code class="descclassname">numpy.fft.</code><code class="descname">ihfft</code><span class="sig-paren">(</span><em>a</em>, <em>n=None</em>, <em>axis=-1</em>, <em>norm=None</em><span class="sig-paren">)</span><a class="reference external" href="http://github.com/numpy/numpy/blob/v1.11.3/numpy/fft/fftpack.py#L539-L595"><span class="viewcode-link">[source]</span></a></span></dt>
<dd><p><span class="yiyi-st" id="yiyi-14">计算具有厄密对称性的信号的逆FFT。</span></p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name">
<col class="field-body">
<tbody valign="top">
<tr class="field-odd field"><th class="field-name"><span class="yiyi-st" id="yiyi-15">参数：</span></th><td class="field-body"><p class="first"><span class="yiyi-st" id="yiyi-16"><strong>a</strong>：array_like</span></p>
<blockquote>
<div><p><span class="yiyi-st" id="yiyi-17">输入数组。</span></p>
</div></blockquote>
<p><span class="yiyi-st" id="yiyi-18"><strong>n</strong>：int，可选</span></p>
<blockquote>
<div><p><span class="yiyi-st" id="yiyi-19">逆FFT的长度。</span><span class="yiyi-st" id="yiyi-20">要使用的输入中沿变换轴的点数。</span><span class="yiyi-st" id="yiyi-21">如果<em class="xref py py-obj">n</em>小于输入的长度，则输入被裁剪。</span><span class="yiyi-st" id="yiyi-22">如果它较大，输入将用零填充。</span><span class="yiyi-st" id="yiyi-23">如果未给出<em class="xref py py-obj">n</em>，则使用沿由<em class="xref py py-obj">轴</em>指定的轴的输入长度。</span></p>
</div></blockquote>
<p><span class="yiyi-st" id="yiyi-24"><strong>axis</strong>：int，可选</span></p>
<blockquote>
<div><p><span class="yiyi-st" id="yiyi-25">用于计算逆FFT的轴。</span><span class="yiyi-st" id="yiyi-26">如果未给出，则使用最后一个轴。</span></p>
</div></blockquote>
<p><span class="yiyi-st" id="yiyi-27"><strong>norm</strong>：{None，“ortho”}，可选</span></p>
<blockquote>
<div><div class="versionadded">
<p><span class="yiyi-st" id="yiyi-28"><span class="versionmodified">版本1.10.0中的新功能。</span></span></p>
</div>
<p><span class="yiyi-st" id="yiyi-29">规范化模式（参见<a class="reference internal" href="../routines.fft.html#module-numpy.fft" title="numpy.fft"><code class="xref py py-obj docutils literal"><span class="pre">numpy.fft</span></code></a>）。</span><span class="yiyi-st" id="yiyi-30">默认值为None。</span></p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name"><span class="yiyi-st" id="yiyi-31">返回：</span></th><td class="field-body"><p class="first"><span class="yiyi-st" id="yiyi-32"><strong>out</strong>：complex ndarray</span></p>
<blockquote class="last">
<div><p><span class="yiyi-st" id="yiyi-33">未指定沿<em class="xref py py-obj">轴</em>指示的轴变换的截断或零填充输入，如果<em class="xref py py-obj">轴</em>指定最后一个输入。</span><span class="yiyi-st" id="yiyi-34">If <em class="xref py py-obj">n</em> is even, the length of the transformed axis is <code class="docutils literal"><span class="pre">(n/2)+1</span></code>. </span><span class="yiyi-st" id="yiyi-35">如果<em class="xref py py-obj">n</em>是奇数，则长度为<code class="docutils literal"><span class="pre">(n+1)/2</span></code>。</span></p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title"><span class="yiyi-st" id="yiyi-36">也可以看看</span></p>
<p class="last"><span class="yiyi-st" id="yiyi-37"><a class="reference internal" href="numpy.fft.hfft.html#numpy.fft.hfft" title="numpy.fft.hfft"><code class="xref py py-obj docutils literal"><span class="pre">hfft</span></code></a>，<a class="reference internal" href="numpy.fft.irfft.html#numpy.fft.irfft" title="numpy.fft.irfft"><code class="xref py py-obj docutils literal"><span class="pre">irfft</span></code></a></span></p>
</div>
<p class="rubric"><span class="yiyi-st" id="yiyi-38">笔记</span></p>
<p><span class="yiyi-st" id="yiyi-39"><a class="reference internal" href="numpy.fft.hfft.html#numpy.fft.hfft" title="numpy.fft.hfft"><code class="xref py py-obj docutils literal"><span class="pre">hfft</span></code></a> / <a class="reference internal" href="#numpy.fft.ihfft" title="numpy.fft.ihfft"><code class="xref py py-obj docutils literal"><span class="pre">ihfft</span></code></a>是类似于<a class="reference internal" href="numpy.fft.rfft.html#numpy.fft.rfft" title="numpy.fft.rfft"><code class="xref py py-obj docutils literal"><span class="pre">rfft</span></code></a> / <a class="reference internal" href="numpy.fft.irfft.html#numpy.fft.irfft" title="numpy.fft.irfft"><code class="xref py py-obj docutils literal"><span class="pre">irfft</span></code></a>的对，但是对于相反的情况： Hermitian在时域中的对称性，并且在频域中是真实的。</span><span class="yiyi-st" id="yiyi-40">所以这里是<a class="reference internal" href="numpy.fft.hfft.html#numpy.fft.hfft" title="numpy.fft.hfft"><code class="xref py py-obj docutils literal"><span class="pre">hfft</span></code></a>，如果它是奇数，你必须提供结果的长度：<code class="docutils literal"><span class="pre">ihfft（hfft（a），</span> <span class="pre">len a））</span> <span class="pre">==</span> <span class="pre">a</span></code>。</span></p>
<p class="rubric"><span class="yiyi-st" id="yiyi-41">例子</span></p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">spectrum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span> <span class="mi">15</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">ifft</span><span class="p">(</span><span class="n">spectrum</span><span class="p">)</span>
<span class="go">array([ 1.+0.j,  2.-0.j,  3.+0.j,  4.+0.j,  3.+0.j,  2.-0.j])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">ihfft</span><span class="p">(</span><span class="n">spectrum</span><span class="p">)</span>
<span class="go">array([ 1.-0.j,  2.-0.j,  3.-0.j,  4.-0.j])</span>
</pre></div>
</div>
</dd></dl>
